# Swift 1.5 Qwen3.8-27B vs WeVisOne-VL-4B (slim)

> Swift 1.5 Qwen3.8-27B and WeVisOne-VL-4B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

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Swift 1.5 Qwen3.8-27B and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-4B leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- Swift 1.5 Qwen3.8-27B: C 61, rank #57 of 160 open-weight models · https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b.min.md
- WeVisOne-VL-4B: C 61, rank #84 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md
- Swift 1.5 Qwen3.8-27B, good for Agents on a 24 to 32 GB GPU or Mac that want Qwen3.8-27B's skills with shorter thinking, for teams under US$1M revenue or research. Ahead on Reach, 80 against 70.
- WeVisOne-VL-4B, good for Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence Ahead on Risk, 55 against 50; Agent interface, 70 against 60; Documentation and upkeep, 48 against 43.

| Category | Swift 1.5 Qwen3.8-27B | WeVisOne-VL-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 80 | 70 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 60 | 70 |
| Documentation and upkeep (10%) | 43 | 48 |

| Fact (where they differ) | Swift 1.5 Qwen3.8-27B | WeVisOne-VL-4B |
| --- | --- | --- |
| Vendor | UkisAI | Tencent (WeChat Vision) |
| Licence | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base | Apache-2.0 |
| Last release | 2026-09-24 | 2026-10-27 |
| Terms last updated | no date given | not read yet |
| Customer content may train models | not found in the text | not read yet |
| Terms restrict automated access | not found in the text | not read yet |
| Terms restrict benchmarking | not found in the text | not read yet |
| Terms or service can change without notice | not found in the text | not read yet |
| Arbitration or class-action waiver | not found in the text | not read yet |
| Agent reviews | 2.3/5 (4) | none |

Why the edge:
- Reach, Swift 1.5 Qwen3.8-27B +10: Swift 1.5 Qwen3.8-27B 80 (Memory 25 of 30, UkisAI's 4-bit MLX build is 15.9 GB and its 4-bit GGUF with the vision projector 16.4 GB, both under 20 GB with a fifth added, which fits 24 GB…); WeVisOne-VL-4B 70 (Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 9.7 GB, and 9.7 GB × 1.2 = 11.6 GB, within 16 GB.)
- Agent interface, WeVisOne-VL-4B +10: Swift 1.5 Qwen3.8-27B 60 (Chat template 20, the base's Jinja file.); WeVisOne-VL-4B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)
- Risk, WeVisOne-VL-4B +5: Swift 1.5 Qwen3.8-27B 50 (File safety 25, safetensors only and nothing from the repository runs.); WeVisOne-VL-4B 55 (File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it.)
- Documentation and upkeep, WeVisOne-VL-4B +5: Swift 1.5 Qwen3.8-27B 43 (Model card 8 of 20, intended use, parameters and context only.); WeVisOne-VL-4B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)